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Mucking around with maps, schools and ethnicity in NZ

Luis

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I’ve been having a conversation for a while with @kamal_hothi and @aschiff on maps, schools, census, making NZ data available, etc. This post documents some basic steps I used for creating a map on ethnic diversity in schools at the census-area-unit level. This “el quicko” version requires 3 ingredients:

We’ll read the school directory data, aggregate ethnicity information to calculate the Herfindahl–Hirschman Index of diversity and then plot it.

# School directory
direc = read.csv('directory-school-current.csv', skip = 3)
 
# Total number of students for each ethnicity by Census Area Unit 
hhi = aggregate(cbind(European..Pakeha, Maori, Pasifika, Asian, MELAA, Other) ~ 
                Census.Area.Unit, data = direc, FUN = sum, na.rm = TRUE)
 
# Function to calculate 
index = function(x) {
  total = sum(x, na.rm = TRUE)
  frac = x/total
  h = sum(frac^2)
  hn = if(total > 1, (h - 1/total)/(1 - 1/total), 1)
  return(hn)
}
 
Calculate the index for each area
hhi$hn = apply(hhi[,2:7], 1, index)
 
# Write data to use in QGis later
write.csv(hhi, 'diversity-index-census-area-unit.csv', quote = FALSE,
          row.names = FALSE)

Then I moved to create a map in R, for the sake of it:

library(rgdal) # for readOGR
library(sp)    # for spplot
 
# Reading shapefile
cau = readOGR(dsn='/Users/lap44/Dropbox/research/2015/census/2014 Digital Boundaries Generlised Clipped',
              layer='AU2014_GV_Clipped')
 
# Joining with school ethnicity data (notice we refer to @data, as cau contains spatial info as well)
cau@data = data.frame(cau@data, 
                      hhi[match(cau@data[,"AU2014_NAM"], hhi[,"Census.Area.Unit"]),])
 
# Limiting map to the area around Christchurch
spplot(cau, zcol = "hn", xlim = c(1540000, 1590000), 
       ylim= c(5163000, 5198000))

And we get a plot like this:

Ethnic diversity in schools at the Census Area Unit level (0 very diverse, 1 not diverse at all).

Just because it is Monday down under.

P.S. Using the diversity-index-census-area-unit.csv and joining it with the shapefile in QGIS one can get something prettier (I have to work on matching the color scales):

Similar map produced with point and click in QGIS.

Map rendering is so much faster in QGIS than in R! Clearly the system has been optimized for this user case.

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